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2dd55b87
编写于
12月 17, 2018
作者:
S
shippingwang
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Add shuffle_channel_op
上级
30aad884
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
385 addition
and
142 deletion
+385
-142
paddle/fluid/operators/shuffle_channel_op.cc
paddle/fluid/operators/shuffle_channel_op.cc
+126
-0
paddle/fluid/operators/shuffle_channel_op.cu
paddle/fluid/operators/shuffle_channel_op.cu
+24
-0
paddle/fluid/operators/shuffle_channel_op.h
paddle/fluid/operators/shuffle_channel_op.h
+101
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+71
-142
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+9
-0
python/paddle/fluid/tests/unittests/test_shuffle_channel_op.py
...n/paddle/fluid/tests/unittests/test_shuffle_channel_op.py
+54
-0
未找到文件。
paddle/fluid/operators/shuffle_channel_op.cc
0 → 100644
浏览文件 @
2dd55b87
/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/shuffle_channel_op.h"
namespace
paddle
{
namespace
operators
{
class
ShuffleChannelOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
-
>
HasInput
(
"X"
),
"Input(X) of ShuffleChannelOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Out"
),
"Output(Out) of ShuffleChannelOp should not be null."
);
auto
input_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE
(
input_dims
.
size
()
==
4
,
"The layout of input is NCHW."
);
// ENFORCE group
auto
group
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"group"
);
ctx
->
SetOutputDim
(
"Out"
,
input_dims
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
());
}
};
class
ShuffleChannelOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), "
"the input feature data of ShuffleChannelOp, the layout is NCHW."
);
AddOutput
(
"Out"
,
"(Tensor, default Tensor<float>), the output of "
"ShuffleChannelOp. The layout is NCHW."
);
AddAttr
<
int
>
(
"group"
,
"the number of groups."
)
.
SetDefault
(
1
)
.
AddCustomChecker
([](
const
int
&
group
)
{
PADDLE_ENFORCE_GE
(
group
,
1
,
"group should be larger than 0."
);
});
AddComment
(
R"DOC(
Shuffle Channel operator
This operator obtains the group convolutional layer with channels shuffled.
First, divide the input channels in each group into several subgroups,
then, feed each group in the next layer with different subgroups.
According to the paper, "Suppose a convolution layer with g groups
whose output has g x n channels, first reshape the output channel dimension into(g,n),
transposing and then flattening it back as the input of next layer. "
Shuffle channel operation makes it possible to build more powerful structures
with multiple group convolutional layers.
please get more information from the following paper:
https://arxiv.org/pdf/1707.01083.pdf
)DOC"
);
}
};
// Grad
class
ShuffleChannelOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@Grad) should not be null"
)
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Output(X@Grad) should not be null"
);
auto
input_dims
=
ctx
->
GetInputDim
(
"X"
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
input_dims
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
type
()),
ctx
.
device_context
());
}
};
}
// namespace operators
}
// namespace paddle
// how to write gpu kernal
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
shufflechannel
,
ops
::
ShuffleChannelOp
,
ops
::
ShuffleChannelOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
// paddle::framework::EmptyGradOpMaker);
REGISTER_OPERATOR
(
shufflechannel_grad
,
ops
::
ShuffleChannelGradOp
);
REGISTER_OP_CPU_KERNEL
(
shufflechannel
,
ops
::
ShuffleChannelOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ShuffleChannelOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
shufflechannel_grad
,
ops
::
ShuffleChannelGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ShuffleChannelGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/shuffle_channel_op.cu
0 → 100644
浏览文件 @
2dd55b87
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/shuffle_channel_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
shufflechannel
,
ops
::
ShuffleChannelOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
ops
::
ShuffleChannelOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
shufflechannel_grad
,
ops
::
ShuffleChannelOpGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
ops
::
ShuffleChannelOpGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/shuffle_channel_op.h
0 → 100644
浏览文件 @
2dd55b87
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
class
ShuffleChannelOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
group
=
ctx
.
Input
<
framework
::
Tensor
>
(
"group"
);
auto
input_dims
=
input
->
dims
();
auto
num
=
input_dims
[
0
];
auto
channel
=
input_dims
[
1
];
auto
height
=
input_dims
[
2
];
auto
weight
=
input_dims
[
3
];
auto
feature_map_size
=
channel
*
height
*
weight
;
auto
sp_sz
=
height
*
weight
;
int
group_row
=
group
;
int
group_column
=
channels
/
group_row
;
const
T
*
input_data
=
input
->
data
<
T
>
();
T
*
output_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
for
(
int
n
=
0
;
n
<
num
;
++
n
)
{
output_data_temp
=
output_data
+
n
*
feature_map_size
;
input_data_temp
=
input_data
+
n
*
feature_map_size
;
for
(
int
i
=
0
;
i
<
group_row
;
++
i
)
{
for
(
int
j
=
0
;
j
<
group_column
;
++
j
)
{
const
auto
*
p_i
=
input_data_temp
+
(
i
*
group_column
+
j
)
*
sp_sz
;
auto
*
p_o
=
output_data_temp
+
(
j
*
group_row
+
i
)
*
sp_sz
;
memcpy
(
p_o
,
p_i
,
sizeof
(
Dtype
)
*
sp_sz
);
}
}
}
return
;
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
ShuffleChannelGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
group
=
ctx
.
Input
<
framework
::
Tensor
>
(
"group"
);
auto
input_dims
=
input
->
dims
();
auto
num
=
input_dims
[
0
];
auto
channel
=
input_dims
[
1
];
auto
height
=
input_dims
[
2
];
auto
weight
=
input_dims
[
3
];
auto
feature_map_size
=
channel
*
height
*
weight
;
auto
sp_sz
=
height
*
weight
;
int
group_row
=
group
;
int
group_column
=
channels
/
group_row
;
auto
*
output_grad
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
input_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
for
(
int
n
=
0
;
n
<
num
;
++
n
)
{
output_grad_temp
=
output_grad_data
+
n
*
feature_map_size
;
input_grad_temp
=
input_grad_data
+
n
*
feature_map_size
;
for
(
int
i
=
0
;
i
<
group_row
;
++
i
)
{
for
(
int
j
=
0
;
j
<
group_column
;
++
j
)
{
const
auto
*
p_i
=
output_grad_temp
+
(
i
*
group_column
+
j
)
*
sp_sz
;
auto
*
p_o
=
input_grad_temp
+
(
j
*
group_row
+
i
)
*
sp_sz
;
memcpy
(
p_o
,
p_i
,
sizeof
(
Dtype
)
*
sp_sz
);
}
}
}
return
;
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
2dd55b87
...
...
@@ -31,148 +31,37 @@ from functools import reduce
from
..
import
core
__all__
=
[
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'bpr_loss'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'conv3d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'sequence_unpad'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'sequence_slice'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'hsigmoid'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'group_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'squeeze'
,
'unsqueeze'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'pad_constant_like'
,
'label_smooth'
,
'roi_pool'
,
'roi_align'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'resize_nearest'
,
'gather'
,
'scatter'
,
'sequence_scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'selu'
,
'log'
,
'crop'
,
'rank_loss'
,
'margin_rank_loss'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
'prelu'
,
'brelu'
,
'leaky_relu'
,
'soft_relu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'scale'
,
'elementwise_add'
,
'elementwise_div'
,
'elementwise_sub'
,
'elementwise_mul'
,
'elementwise_max'
,
'elementwise_min'
,
'elementwise_pow'
,
'uniform_random_batch_size_like'
,
'gaussian_random'
,
'sampling_id'
,
'gaussian_random_batch_size_like'
,
'sum'
,
'slice'
,
'shape'
,
'logical_and'
,
'logical_or'
,
'logical_xor'
,
'logical_not'
,
'clip'
,
'clip_by_norm'
,
'mean'
,
'mul'
,
'sigmoid_cross_entropy_with_logits'
,
'maxout'
,
'space_to_depth'
,
'affine_grid'
,
'sequence_reverse'
,
'affine_channel'
,
'similarity_focus'
,
'hash'
,
'grid_sampler'
,
'log_loss'
,
'add_position_encoding'
,
'bilinear_tensor_product'
,
'merge_selected_rows'
,
'get_tensor_from_selected_rows'
,
'lstm'
,
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'bpr_loss'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'conv3d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'sequence_unpad'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'sequence_slice'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'hsigmoid'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'group_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'squeeze'
,
'unsqueeze'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'pad_constant_like'
,
'label_smooth'
,
'roi_pool'
,
'roi_align'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'resize_nearest'
,
'gather'
,
'scatter'
,
'sequence_scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'selu'
,
'log'
,
'crop'
,
'rank_loss'
,
'margin_rank_loss'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
'prelu'
,
'brelu'
,
'leaky_relu'
,
'soft_relu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'scale'
,
'elementwise_add'
,
'elementwise_div'
,
'elementwise_sub'
,
'elementwise_mul'
,
'elementwise_max'
,
'elementwise_min'
,
'elementwise_pow'
,
'uniform_random_batch_size_like'
,
'gaussian_random'
,
'sampling_id'
,
'gaussian_random_batch_size_like'
,
'sum'
,
'slice'
,
'shape'
,
'logical_and'
,
'logical_or'
,
'logical_xor'
,
'logical_not'
,
'clip'
,
'clip_by_norm'
,
'mean'
,
'mul'
,
'sigmoid_cross_entropy_with_logits'
,
'maxout'
,
'space_to_depth'
,
'affine_grid'
,
'sequence_reverse'
,
'affine_channel'
,
'similarity_focus'
,
'hash'
,
'grid_sampler'
,
'log_loss'
,
'add_position_encoding'
,
'bilinear_tensor_product'
,
'merge_selected_rows'
,
'get_tensor_from_selected_rows'
,
'lstm'
,
'shufflechannel'
]
kIgnoreIndex
=
-
100
...
...
@@ -9122,3 +9011,43 @@ def get_tensor_from_selected_rows(x, name=None):
outputs
=
{
'Out'
:
out
},
attrs
=
{})
return
out
def
shuffle_channel
(
x
,
group
=
1
,
name
=
None
):
"""
**Shuffle Channel Operator**
This operator obtains the group convolutional layer with channels shuffled.
First, divide the input channels in each group into several subgroups,
then, feed each group in the next layer with different subgroups.
Shuffle channel operation makes it possible to build more powerful structures
with multiple group convolutional layers.
Args:
x: The input tensor variable.
Returns:
Variable: channel shuffled tensor variable.
Raises:
ValueError: If group in not a int type variable.
Examples:
.. code-block:: python
"""
helper
=
LayerHelper
(
"shuffle_channel"
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
helper
.
intput_dtype
(
'x'
))
if
not
isinstance
(
group
,
int
):
raise
TypeError
(
"group must be int type"
)
helper
.
append_op
(
type
=
"shuffle_channel"
,
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
out
},
attrs
=
{
"group"
:
group
})
return
out
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
2dd55b87
...
...
@@ -982,6 +982,15 @@ class TestBook(unittest.TestCase):
print
(
str
(
program
))
def
test_shuffle_channel
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
"x"
,
shape
=
[
10
,
32
,
16
,
16
],
dtype
=
"float32"
)
group
=
layers
.
data
(
name
=
"group"
,
shape
=
[
1
],
dtype
=
"int32"
)
out
=
layers
.
shuffle_channel
(
x
,
group
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_shuffle_channel_op.py
0 → 100644
浏览文件 @
2dd55b87
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
sys
import
math
from
op_test
import
OpTest
import
paddle.fluid.core
as
core
class
TestShuffleChannelOp
(
OpTest
):
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'output'
)
def
setUp
(
self
):
self
.
op_type
=
"shuffle_channel"
self
.
batch_size
=
10
self
.
input_channels
=
16
self
.
layer_h
=
32
self
.
layer_w
=
32
self
.
group
=
4
self
.
x
=
np
.
random
.
random
(
(
self
.
batch_size
,
self
.
input_channels
,
self
.
layer_h
,
self
,
layer_w
)).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
self
.
x
}
self
.
attrs
=
{
'group'
:
self
.
group
}
n
,
c
,
h
,
w
=
self
.
x
.
shape
input_reshaped
=
np
.
reshape
(
self
.
x
,
(
-
1
,
self
.
group
,
c
//
self
.
group
,
h
,
w
))
input_transposed
=
np
.
transpose
(
input_reshaped
,
(
0
,
2
,
1
,
3
,
4
))
self
.
outputs
=
np
.
reshape
(
input_transposed
,
(
-
1
,
c
,
h
,
w
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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